class_docstr: Principal Component Analysis (PCA).
  
  Parameters
  ----------
  rescale_with_mean
      Whether or not to subtract each column's mean before performing SVD.
  rescale_with_std
      Whether or not to standardize each column before performing SVD.
  n_components
      The number of principal components to compute.
  n_iter
      The number of iterations used for computing the SVD.
  copy
      Whether nor to perform the computations inplace.
  check_input
      Whether to check the coherence of the inputs or not.
docstr: Returns the row standard coordinates.
  
  The row standard coordinates are obtained by dividing each row principal coordinate by it's
  associated eigenvalue.
docstr_tok: ["Returns","the","row","standard","coordinates",".","The","row","standard","coordinates","are","obtained","by","dividing","each","row","principal","coordinate","by","it's","associated","eigenvalue","."]
code: def row_standard_coordinates(self, X: pd.DataFrame = None):
      """Returns the row standard coordinates.
  
      The row standard coordinates are obtained by dividing each row principal coordinate by it's
      associated eigenvalue.
  
      """
      return self.row_coordinates(X).div(
          self.eigenvalues_, axis="columns"
      )
  
imports: from __future__ import annotations
  import functools
  import altair
  import numpy
  import pandas
  import sklearn.base
  import sklearn.utils
  from sklearn import preprocessing
  from prince import svd, utils
label_desc: Use image node_id 10 for calling the PCA obj's underlying member method code with example usage: obj.row_standard_coordinates(X) and returns: self